Enhancing Clustering Algorithm to Plan Efficient Mobile Network

TL;DR

This paper introduces a new clustering algorithm, CWN-PAM, designed to optimize mobile network base station placement, demonstrating improved effectiveness and flexibility through real case study results.

Contribution

The paper presents the development and implementation of CWN-PAM, an advanced clustering algorithm tailored for efficient mobile network planning.

Findings

CWN-PAM outperforms previous algorithms in real case studies.

The modified algorithm effectively balances service quality and cost.

Flexible adaptation to various network planning constraints.

Abstract

With the rapid development in mobile network effective network planning tool is needed to satisfy the need of customers. However, deciding upon the optimum placement for the base stations (BS) to achieve best services while reducing the cost is a complex task requiring vast computational resource. This paper addresses antenna placement problem or the cell planning problem, involves locating and configuring infrastructure for mobile networks. The Cluster Partitioning Around Medoids (PAM) original algorithm has been modified and a new algorithm M-PAM (Modified-Partitioning Around Medoids) has been proposed by the authors in a recent work. In the present paper, the M-PAM algorithm is modified and a new algorithm CWN-PAM (Clustering with Weighted Node-Partitioning Around Medoids) has been proposed to satisfy the requirements and constraints. Implementation of this algorithm to a real case study is presented. Results demonstrate the effectiveness and flexibility of the modifying algorithm in tackling the important problem of mobile network planning.